ID 原文 译文
3753 针对计算任务卸载模式的选择及边云资源分配的问题，设计了一种基于次模理论的贪心算法并充分利用了云端以及边缘端的计算和通信资源。 In order to tackle the problems of computation offloading mode selection and edge-cloud resourceallocation, a greedy algorithm based on submodular theory was developed by fully exploiting the computing and commu-nication resources of cloud and edge.
3754 仿真结果表明，所提方案能够有效降低计算任务执行的时延和能耗，且当多用户卸载计算任务时，所提方案在资源受限的条件下仍然能够保持稳定的系统性能。 The simulation results demonstrate that the proposed scheme effectively reduces thedelay and energy consumption of computing tasks. Additionally, when computing tasks are offloaded to edge and cloudfrom devices, the proposed scheme still maintains stable system utilities under ultra-limited resources.
3755 为解决移动边缘计算（MEC）网络中面向用户的服务功能链部署（SFC）算法系统开销过大、业务拥塞严重等问题，针对部署 MEC 服务器的多基站多用户边缘网络，提出了一种面向内容的联合无线多播的 SFC 部署算法。 To resolve the excessive system overhead and serious traffic congestion in user-oriented service function chain(SFC) embedding in mobile edge computing (MEC) networks, a content-oriented joint wireless multicast and SFC em-bedding algorithm was proposed for the multi-base station and multi-user edge networks with MEC servers.
3756 综合考虑数据流、服务器功能维护功耗、服务器功能服务功耗和无线传输功耗 4 种系统开销，建立波束成形设计和 SFC 映射的联合优化模型。 By involving four kinds of system overhead, including service flow, server function sustaining power, server function service powerand wireless transmission power, an optimization model was proposed to jointly design SFC embedding with multicast beam forming.
3757 首先，利用拉格朗日对偶分解技术，将优化问题解耦为 SFC 部署和波束成形设计 2 个独立子问题； Firstly, with Lagrangian dual decomposition, the problem was decoupled into two independent subprob-lems, namely, SFC embedding and multicast beamforming.
3758 其次，利用基于 L p 范数惩罚项的连续凸近似算法，将整数形式的 SFC 部署问题松弛为一个等价线性规划问题； Secondly, with the L p  norm penalty term-based successiveconvex approximation algorithm, the integer programming-based SFC embedding problem was relaxed to an equivalentlinear programming one.
3759 最后，利用路径跟随技术，将非凸波束向量优化问题转化为一系列凸优化子问题。 Finally, the non-convex beamforming optimization problem was transformed into a series ofconvex ones via the path following technique.
3760 仿真结果表明，所提算法具有较好收敛性能，并在系统开销方面优于传统的最优单播 SFC 部署算法和随机多播 SFC 部署算法。 Simulation results revealed that the proposed algorithm has good conver-gence, and is superior to both the optimal SFC embedding with unicasting and random SFC embedding with multicastingin terms of system overhead.
3761 考虑不同物联网系统的异构性以及集中化数据处理平台单点故障等问题，提出一种基于区块链技术的去中心化物联网数据共享和存储方案。 Considering the heterogeneity of various IoT system and the single point failure of centralized data-processingplatform, a decentralized IoT data sharing and storage method based on blockchain technology was proposed.
3762 通过存储证明（PoS）的共识机制，实现区块共识和共享数据的分布式存储。 The blockconsensus and decentralized storage of shared data were realized through the PoS consensus mechanism.